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AI can now outperform doctors at detecting breast cancer. Here's why it won't replace them.

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Breast cancer affects way too many of us. In the US, one in eight women will develop it in their lifetimes. But encouraging new research shows that artificial intelligence can help with early detection. An AI system developed by Google Health, Google-owned DeepMind, and several medical centers is so good at detecting breast cancer that it can outperform actual doctors, according to a paper published this week in the journal Nature. The AI analyzes mammograms -- the X-rays commonly used to check for breast cancer -- to determine whether the disease is present. Researchers found that the AI system reduced false positives by 5.7 percent for US women -- a significant improvement, when you consider how distressing it would be to be told you have cancer when you actually do not.


AI Can Outperform Doctors. So Why Don't Patients Trust It?

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Our recent research indicates that patients are reluctant to use health care provided by medical artificial intelligence even when it outperforms human doctors. Because patients believe that their medical needs are unique and cannot be adequately addressed by algorithms. To realize the many advantages and cost savings that medical AI promises, care providers must find ways to overcome these misgivings. Medical artificial intelligence (AI) can perform with expert-level accuracy and deliver cost-effective care at scale. IBM's Watson diagnoses heart disease better than cardiologists do.


Computers will outperform doctors at diagnosing illnesses, says government technology adviser

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In 2014, the government brought in a new curriculum, which included coding lessons for children. But Prof Susskind said that the development of new, "self-coding" systems meant that such lessons were obsolete. He added: "I belong to the school of thought who don't believe it's a particularly great use of people's time and energy to code. Our thesis is that the next generation of systems will be writing themselves. Automatic code generation is already very common. "Low-level code generation is actually a great intellectual exercise, it's a bit like studying logic, but I don't believe that people learning to code in school will find in seven or eight years that they'll be employable for that reason alone.